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license: cc-by-sa-4.0
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---
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---
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license: cc-by-sa-4.0
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---
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# **koOpenChat-sftπ§**
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## Support Me
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μλνΈλΌλ κ°μΈ νλ‘μ νΈλ‘, 1μΈμ μμμΌλ‘ κ°λ°λκ³ μμ΅λλ€. λͺ¨λΈμ΄ λ§μμ λμ
¨λ€λ©΄ μ½κ°μ μ°κ΅¬λΉ μ§μμ μ΄λ¨κΉμ?
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[<img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy me a Coffee" width="217" height="50">](https://www.buymeacoffee.com/mwell)
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Wanna be a sponser? (Please) Contact me on Telegram **AlzarTakkarsen**
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# **Model Details**
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**Base Model**
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OpenChat3.5
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**Trained On**
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A100 80GB * 1
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**Instruction format**
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It follows [ChatML](https://github.com/openai/openai-python/blob/main/chatml.md) format and **Alpaca(No-Input)** format.
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# **Model Benchmark**
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None
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# **Implementation Code**
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Since, chat_template already contains insturction format above.
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You can use the code below.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained("maywell/koOpenChat-sft")
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tokenizer = AutoTokenizer.from_pretrained("maywell/koOpenChat-sft")
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messages = [
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{"role": "user", "content": "λ°λλλ μλ νμμμ΄μΌ?"},
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]
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encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
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model_inputs = encodeds.to(device)
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model.to(device)
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generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
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decoded = tokenizer.batch_decode(generated_ids)
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print(decoded[0])
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```
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